A Method of Topic Extraction Based on WordTag and LDA
Wanting Zhou · 2022
Extracting topics from documents is a common task in the field of Natural Language Processing (NLP). Both traditional feature extraction methods and various topic models can be used for such tasks of finding key information. Latent Dirichlet Allocation (LDA) is one of the classic topic models. The recently popular deep learning pre-training model has greatly improved the effect of various NLP tasks, and the method of applying the pre-training model to downstream tasks has research value. The application of Chinese pre-trained models also requires more attempts. This paper believes that combining deep learning technology can help to improve traditional methods and find key information. Therefore, based on the deep learning knowledge tagging model WordTag, we combine it with the results of knowledge tagging and LDA topic model, and propose a topic extraction method based on word classification tagging (WordTag and Latent Dirichlet Allocation, WT-LDA). Experiments show that the method proposed in this paper is more effective than other methods of topic extraction.